• DocumentCode
    3252416
  • Title

    Compumetric forecasting of crude oil prices

  • Author

    Kaboudan, M.A.

  • Author_Institution
    MS&IS, Fogelsville, PA, USA
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    283
  • Abstract
    This paper contains short term monthly forecasts of crude oil prices using compumetric methods. Compumetric forecasting methods are ones that use computers to identify the underlying model that produces the forecast. Typically, forecasting models are designed or specified by humans rather than machines. Compumetric methods are applied to determine whether models they provide produce reliable forecasts. Forecasts produced by two compumetric methods-genetic programming and artificial neural networks-are compared and evaluated relative to a random walk type of prediction. The results suggest that genetic programming has advantage over random walk predictions while the neural network forecast proved inferior
  • Keywords
    commodity trading; forecasting theory; neural nets; compumetric forecasting; crude oil prices; forecasting models; monthly forecasts; random walk type; Artificial neural networks; Demand forecasting; Econometrics; Economic forecasting; Genetic programming; Load forecasting; Petroleum; Predictive models; US Department of Energy; Weather forecasting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
  • Conference_Location
    Seoul
  • Print_ISBN
    0-7803-6657-3
  • Type

    conf

  • DOI
    10.1109/CEC.2001.934402
  • Filename
    934402